Prognostic factor analysis for breast cancer

using gene expression profiles

Soobok Joe,Hojung Nam§

School of Information and Communication Department, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju, Republic of Korea

§Corresponding author

Email addresses:

Soobok Joe:

Hojung Nam:

Additional file 1:Figure S1. Selection of prognostic candidate-genes based on log-rank test

To identify high/low expressed genes based on patient’s poor survival, we implemented a log-rank test and used an expression fold-change value of patient groups in the first quartile and forth quartile of the gene expression distribution. This process was implemented per each gene. Hazard ratio was calculated between first and forth quartile patient groups and adjusted p-value cutoff was determined as0.001. Therefore, if hazard ratio is significant and patients’ expression fold-change value (first/forth) is greater than 1.5, weselected the gene as a high-expressed gene in poor survival. Similarly, if hazard ratio is significant and an expression fold-change (first/forth) is less than 0.5, weselected the gene as a low-expressed gene in poor survival.

Additional File 1:Table S1. The prediction of patients’ outcome based on log-rank test according to varied correlation thresholds.

Matched
Cluster / Correlation (Pearson) / GSE2034 / GSE25066 / GSE3494
Positive / Negative / HR / P / HR / P / HR / P
1 / 1 / 0.4 / -0.4 / 1.800 / 0.004 / 3.648 / 0.000 / 3.279 / 0.000
1 / 1 / 0.4 / -0.5 / 1.800 / 0.004 / 2.766 / 0.000 / 2.704 / 0.000
1 / 1 / 0.4 / -0.6 / 1.853 / 0.002 / 2.856 / 0.000 / 3.039 / 0.000
1 / 1 / 0.5 / -0.4 / 1.488 / 0.052 / 3.572 / 0.000 / 3.642 / 0.000
1 / 1 / 0.5 / -0.5 / 1.546 / 0.033 / 2.563 / 0.000 / 2.165 / 0.007
1 / 1 / 0.6 / -0.4 / 1.222 / 0.351 / 3.738 / 0.000 / 1.940 / 0.022
1 / 1 / 0.6 / -0.5 / 1.852 / 0.002 / 2.368 / 0.000 / 2.729 / 0.000
1 / 1 / 0.7 / -0.4 / 1.353 / 0.145 / 1.491 / 0.045 / 1.953 / 0.020
1 / 1 / 0.7 / -0.5 / 1.551 / 0.031 / 1.550 / 0.027 / 2.646 / 0.001
1 / 2 / 0.4 / -0.4 / 1.544 / 0.033 / 4.005 / 0.000 / 2.368 / 0.002
1 / 2 / 0.5 / -0.4 / 1.503 / 0.047 / 3.580 / 0.000 / 3.025 / 0.000
1 / 2 / 0.6 / -0.4 / 1.220 / 0.354 / 4.060 / 0.000 / 1.867 / 0.031
1 / 2 / 0.6 / -0.5 / 1.160 / 0.506 / 4.070 / 0.000 / 2.128 / 0.008
1 / 2 / 0.7 / -0.4 / 1.491 / 0.051 / 1.550 / 0.027 / 2.127 / 0.008
1 / 2 / 0.7 / -0.5 / 1.322 / 0.181 / 1.516 / 0.037 / 2.511 / 0.001
2 / 1 / 0.4 / -0.4 / 1.525 / 0.039 / 3.218 / 0.000 / 2.665 / 0.001
2 / 1 / 0.4 / -0.5 / 1.224 / 0.347 / 3.040 / 0.000 / 1.729 / 0.060
2 / 1 / 0.5 / -0.4 / 1.312 / 0.195 / 3.423 / 0.000 / 2.083 / 0.010
2 / 1 / 0.5 / -0.5 / 1.415 / 0.092 / 2.889 / 0.000 / 2.133 / 0.008
2 / 1 / 0.6 / -0.4 / 1.452 / 0.069 / 3.642 / 0.000 / 1.938 / 0.021
2 / 1 / 0.6 / -0.5 / 1.764 / 0.005 / 2.028 / 0.000 / 2.519 / 0.001
2 / 1 / 0.7 / -0.4 / 1.414 / 0.092 / 1.768 / 0.004 / 2.755 / 0.000
2 / 1 / 0.7 / -0.5 / 1.337 / 0.162 / 1.879 / 0.001 / 2.049 / 0.012
2 / 2 / 0.4 / -0.4 / 1.079 / 0.770 / 3.208 / 0.000 / 1.276 / 0.446
2 / 2 / 0.4 / -0.5 / 0.946 / 0.851 / 3.774 / 0.000 / 0.971 / 0.977
2 / 2 / 0.4 / -0.6 / 0.946 / 0.851 / 3.774 / 0.000 / 0.971 / 0.977
2 / 2 / 0.5 / -0.4 / 1.201 / 0.400 / 3.422 / 0.000 / 2.504 / 0.001
2 / 2 / 0.5 / -0.5 / 1.325 / 0.178 / 3.099 / 0.000 / 1.549 / 0.139
2 / 2 / 0.5 / -0.6 / 1.333 / 0.168 / 2.975 / 0.000 / 1.844 / 0.034
2 / 2 / 0.6 / -0.4 / 1.280 / 0.242 / 3.253 / 0.000 / 2.205 / 0.006
2 / 2 / 0.7 / -0.4 / 1.627 / 0.016 / 1.330 / 0.161 / 2.133 / 0.008

Two major gene clusters are constructed using maximal clique algorithm with Pearson correlation 0.4 to 0.7 as an edge threshold. These processes were implemented on 413 high-expressed genes and 411 low-expressed genes from log-rank test. For each positive correlation threshold, maximal bi-clique algorithm was used with negative edge threshold -0.4 to -0.6.

Additional file 1:Table S2. The gene list of module 1 including previously defined prognostic factor.

Gene / Authors
High- expressed genes / CHEK1 / Sarah A Andreset. al[1]
FOXM1 / D. C. JIAOet. al [2]
CCNA2 / Tian Gaoet. al [3]
CDC20 / H Karraet. al [4]
TTK / Al-Ejeh Fet. al [5]
CENPA / Cheng Zhanget. al [6], Ashish B. Rajputet. al [7]
KIF2C / Al Muktafi Sadiet. al [8]
BUB1 / AbhikMukherjeeet. al [9] ,LiberoSantarpiaet. al [10]
MCM6 / None
LMNB2 / None
CDC45 / None
ANLN / Al Muktafi Sadiet. al [8]
MCM10 / None
CDCA8 / Jiao DCet. al [2]
MELK / Rong Liuet. al [11]
CCNB2 / EmmanShubbaret. al [12]
CEP55 / Katherine J. Martinet. al [13]
DLGAP5 / Rong Liuet. al [11]
HJURP /
  • Rocío Montes de Ocaet. al [14] , Zhi Huet. al[15]

CDCA5 / None
TRIP13 / None
GTSE1 / None
CDCA3 / None
PRR11 / None
FAM83D / Peter J. Walianet. al [16]
GTPBP4 / None
Low- expressed genes / ESR1 / Aleksandra Markiewiczet. al [17],Sarah A Andreset. al [18]
GATA3 / Franco Izzoet. al [19],Nam K. Yoonet. al [20]
LRIG1 / Patricia A. Thompsonet. al [21]
RABEP1 / Sarah A Andreset. al [18]
CIRBP / None
EVL / Sarah A Andreset. al [18]
WDR19 / None
SCUBE2 /
  1. Chien-Jui Chenget. al [22]

KIF13B / None
TBC1D9 / Sarah A Andreset. al [18]
ANKRA2 / None
DYNLRB2 / None
NME5 / Toshima Z. Parris et. al [23]
CAPN8 / None
CASC1 / None
BBOF1 / None
RUNDC1 / None

In 26 high-expressed genes in module 1, 16 genes are previously defined as prognostic genes, and among 17 low-expressed genes, 8 genes are previously defined as prognostic genes. These genes are listed through the PubMed ( search in terms of (GENE[TIAB] AND (breast cancer[TIAB] OR breast tumor[TIAB]))AND (prognosis [TIAB] OR prognostic[TIAB]) .

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